In recent years the number of mobile computing devices has increased dramatically, creating the need for more advanced mobile and wireless services. Mobile email, walkie-talkie services, multi-player gaming and call following are just a few examples of new applications that are emerging on mobile devices. In addition, mobile users are beginning to demand applications that not only utilize their current location but also share that location information with others. For example, parents wish to keep track of their children, supervisors need to track the location of their companies' delivery vehicles, and business travelers desire to search for nearby pharmacies to pick up prescriptions. All of these examples require the individual to know their own location or that of someone else.
Currently available methods for utilizing consumer location on mobile devices can be classified into three categories. The first category involves utilizing precise coordinates. Applications can acquire very accurate location information using the device's GPS receiver and motion sensors, such as for providing mobile map and driving directions functionality. The second category involves utilizing geographical proximity. In this case, applications can perform certain actions upon detecting users' proximity towards a specific geo location. This can include, for example, applications that provide coupons and sales promotion material distribution, online check-in, and mobile advertising functionality. The third category involves utilizing geo-fencing, that is, applications that detect whether a user is within a certain geographical area as described by a virtual “fence” defined by a user. Examples include mobile asset tracking, child safety applications, mobile advertising, and sales promotion.
A wide variety of different types of wireless communication systems are known. Also, numerous techniques have been developed that deliver advertising or other types of messages to mobile user devices based on the current locations of those devices. Thus, if a given mobile user device is determined to be in close proximity to a particular retail establishment, an advertisement or other message associated with that establishment may be delivered to the mobile user device. Unfortunately, conventional wireless communication systems suffer from a number of significant drawbacks. For example, conventional systems are typically configured in a manner that can lead to excessive location queries or other types of location-related communications between the base stations and the mobile user devices, thereby undermining the ability of the systems to support their primary voice and data traffic functionality.
“Fence crossing” generally refers to determining when a given mobile user has crossed a designated boundary. A fence-crossing event may be used, for example, to control the delivery of a particular message to a given mobile user device, or to control the provision of other types of location-based services. Conventional techniques for dealing with fence crossings fail to provide optimal performance in delivery of location-based services, particularly in mass market, high-volume applications, which can involve many mobile users and many fences per user. For example, conventional techniques often require excessive messaging over an air interface of the wireless network, thereby consuming system resources and adversely impacting system performance. Also, such messaging can result in increased power consumption in the mobile user device, thereby adversely impacting battery life. Accordingly, new techniques are needed that can further improve the delivery of location-based services through enhanced processing of fence-crossing information.
Advertisers and advertising providers attempt to maximize the effectiveness of advertising by targeting certain marketing materials at consumers based on a number of criterion, including time of day, location within an urban environment (e.g., proximity to a particular vendor) and also based on demographics of the particular consumers likely to view the advertising. This kind of targeting advertising is known to substantially increase sales revenues due to a significant increase in advertising value. Typically, targeted advertising is accomplished by associating advertising with advertising outlets in particular urban neighborhoods or locations, such as by mounting advertising posters in certain neighborhoods, or along certain traffic routes and by providing advertising in vehicles that only run at certain times of the day (e.g., rush hour overflow buses).
Currently available methods exhibit a number of limitations, including but not limited to the following:
(i) Limited scalability: geo-proximity areas and geo-fences vary significantly depending on the user and application, and normally include substantial provisioning and maintenance overhead. As an example, a large brand or an advertising agency might have to configure millions of proximity locations or fences in order to capture all locations of a specific category (e.g., restaurant, or residential) in a given country.
(ii) Inability to capture temporal differences: the same set of locations may attract a substantially different set of users depending on day or week, time of day, and local events. Location alone, without the time dimension and understanding of the local scene, has limited applicability and value for businesses.
(iii) Potential disclosure of the mobile user location to third parties: proximity and geo-fencing techniques have the risk of indirect disclosure of the user location to parties such as advertisers and stores. This may cause regulatory concerns by the mobile operators or limited subscriber uptake in the case of direct consumer consent acquisition.
In general, one can distinguish three types of contexts associated with mobile users that represent substantial interest to other businesses (such as retail owners, advertisers, application developers and content owners): (i) internal personal context reflecting a person's thoughts, desires and the like, which is normally expressed by the individuals while performing web search (e.g., Google search) or participating in social networking (e.g., Facebook or Twitter); (ii) the application context, reflecting the subject area of the mobile application being used by the individual, e.g., news, stock market, media, social networking, games, etc.; and (iii) the situational context, reflecting spatial and temporal placement of an individual, e.g., office time, business lunch, family dinner, travel, and so on.
Existing mobile information systems tend to focus exclusively on user's location, their search and social network activities (personal context) and applications themselves (application context), providing little appreciation of a mobile user's overall condition or context. Because of the small screens and keyboards associated with these mobile devices, it is much harder for mobile users to express their information needs and to locate relevant search results on mobile devices. In particular, when web search approaches are applied to mobile platforms, usability and mobile user experience become significant issues. Because of these problems, it is clear that mobile search as a direct translation of conventional web approaches will have limited success in the mobile domain.
The application context itself also exhibits limited applicability. Smartphone users reading news in a business meeting, or playing games on a train to work or in a bar after work, is a common phenomenon. The fact that a user is utilizing a particular application in a given moment in time does not necessarily represent the significance of the application's subject area to that person overall or at a particular point in time. Of the three mobile user contexts described above, the situational context has been the least explored, reducing to mostly location-only techniques, which do not capture full complexity and level of relevance from the consumer's perspective.
What is needed, therefore, are improved techniques that overcome the limitations of existing approaches through a user-based situational context that is defined as a function of space and time.